Sloan School of Managementhttp://hdl.handle.net/1721.1/1777
Thu, 27 Dec 2018 16:59:55 GMT2018-12-27T16:59:55ZSloan School of Managementhttp://dspace.mit.edu:80/bitstream/id/5834/Sloan_201.pcsm.gifhttp://hdl.handle.net/1721.1/1777
Modeling human dynamics and lifestyles using digital traceshttp://hdl.handle.net/1721.1/119356
Modeling human dynamics and lifestyles using digital traces
Xu, Sharon
In this thesis, we present algorithms to model and identify shared patterns in human activity with respect to three applications. First, we propose a novel model to characterize the bursty dynamics found in human activity. This model couples excitation from past events with weekly periodicity and circadian rhythms, giving the first descriptive understanding of mechanisms underlying human behavior. The proposed model infers directly from event sequences both the transition rates between tasks as well as nonhomogeneous rates depending on daily and weekly cycles. We focus on credit card transactions to test the model, and find it performs well in prediction and is a good statistical fit for individuals. Second, using credit card transactions, we identify lifestyles in urban regions and add temporal context to behavioral patterns. We find that these lifestyles not only correspond to demographics, but also have a clear signal with one's social network. Third, we analyze household load profiles for segmentation based on energy consumption, focusing on capturing peak times and overall magnitude of consumption. We propose novel metrics to measure the representative accuracy of centroids, and propose a method that outperforms standard and state of the art baselines with respect to these metrics. In addition, we show that this method is able to separate consumers well based on their solar PV and storage needs, thus helping consumers understand their needs and assisting utilities in making good recommendations.
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 63-69).
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1193562018-01-01T00:00:00ZApplications of healthcare analytics in reducing hospitalization dayshttp://hdl.handle.net/1721.1/119355
Applications of healthcare analytics in reducing hospitalization days
Furtado, Jazmin D. (Jazmin Dahl)
In this thesis, we employ healthcare analytics to inform system-level changes at Massachusetts General Hospital that could lead to a significant reduction in avoidable hospitalization days and improvement in patients outcomes. The first area of focus is around avoidable bed-days in the ICU. Many surgical patients experience non-clinical delays when they transfer from the ICU to a subsequent general care unit where they are expected to continue their recovery. As a result, they spend a longer time in the ICU than necessary. In spite of several studies that suggest out-of-ICU transfer delays are quite common, there is little work that quantifies the impact on patient recovery. Using multiple statistical approaches including regression and matching, we obtain a robust result that suggests that non-clinical transfer delays from the ICU delay the patient's recovery as well as extend the hospital LOS. Specifically, the analysis shows that each day that the patient is delayed in the ICU for non-clinical reasons increases hospital LOS by 0.71 days (p-value < 0.01) and the patient's progress of care by 0.32 days (p-value < 0.01), on average. The second area of focus is concerned with bed-days from heart failure (HF) admissions. Much of the current work in reducing HF hospitalizations promotes interventions after the patient is hospitalized, aiming to prevent subsequent hospitalizations within 30 days. In contrast, we focus on reducing overall hospitalizations from the general HF population. We first analyze the outpatient access for these patients before they are admitted to the hospital (mostly) through the Emergency Department. One of the main findings is that in more than half of these admissions, the patient did not have a completed appointment with any outpatient clinic (Primary Care, Cardiology, or Home Health) during the two weeks prior to hospitalization. This reveals the need for improved outpatient-based preventative measures to manage HF patients. To partially address this challenge, we develop a predictive model using a logistic regression to predict the risk of a HF-related admission within the next six months. The model performs quite well with an out-of-sample AUC of 0.78.
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 108-114).
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1193552018-01-01T00:00:00ZSparse learning : statistical and optimization perspectiveshttp://hdl.handle.net/1721.1/119354
Sparse learning : statistical and optimization perspectives
Dedieu, Antoine
In this thesis, we study the computational and statistical aspects of several sparse models when the number of samples and/or features is large. We propose new statistical estimators and build new computational algorithms - borrowing tools and techniques from areas of convex and discrete optimization. First, we explore an Lq-regularized version of the Best Subset selection procedure which mitigates the poor statistical performance of the best-subsets estimator in the low SNR regimes. The statistical and empirical properties of the estimator are explored, especially when compared to best-subsets selection, Lasso and Ridge. Second, we propose new computational algorithms for a family of penalized linear Support Vector Machine (SVM) problem with a hinge loss function and sparsity-inducing regularizations. Our methods bring together techniques from Column (and Constraint) Generation and modern First Order methods for non-smooth convex optimization. These two components complement each others' strengths, leading to improvements of 2 orders of magnitude when compared to commercial LP solvers. Third, we present a novel framework inspired by Hierarchical Bayesian modeling to predict user session-length on on-line streaming services. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework incorporates flexible parametric/nonparametric models on the covariates and outperforms state-of- the-art estimators in terms of efficiency and predictive performance on real world datasets from the internet radio company Pandora Media Inc.
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 101-109).
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1193542018-01-01T00:00:00ZRegulating exploration in multi-armed bandit problems with time patterns and dying armshttp://hdl.handle.net/1721.1/119353
Regulating exploration in multi-armed bandit problems with time patterns and dying arms
Tracà, Stefano
In retail, there are predictable yet dramatic time-dependent patterns in customer behavior, such as periodic changes in the number of visitors, or increases in customers just before major holidays. The standard paradigm of multi-armed bandit analysis does not take these known patterns into account. This means that for applications in retail, where prices are fixed for periods of time, current bandit algorithms will not suffice. This work provides a framework and methods that take the time-dependent patterns into account. In the corrected methods, exploitation (greed) is regulated over time, so that more exploitation occurs during higher reward periods, and more exploration occurs in periods of low reward. In order to understand why regret is reduced with the corrected methods, a set of bounds on the expected regret are presented and insights into why we would want to exploit during periods of high reward are discussed. When the set of available options changes over time, mortal bandits algorithms have proven to be extremely useful in a number of settings, for example, for providing news article recommendations, or running automated online advertising campaigns. Previous work on this problem showed how to regulate exploration of new arms when they have recently appeared, but they do not adapt when the arms are about to disappear. Since in most applications we can determine either exactly or approximately when arms will disappear, we can leverage this information to improve performance: we should not be exploring arms that are about to disappear. Also for this framework, adaptations of algorithms and regret bounds are provided. The proposed methods perform well in experiments, and were inspired by a high-scoring entry in the Exploration and Exploitation 3 contest using data from Yahoo! Front Page. That entry heavily used time-series methods to regulate greed over time, which was substantially more effective than other contextual bandit methods.
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 65-70).
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1193532018-01-01T00:00:00Z